Sentiment Analysis With H2O, Pyspark and Word2vec On Qubole

Embark on a journey of data analysis and sentiment prediction with Qubole’s innovative solutions. This video unveils the process of ingesting and analyzing raw Amazon review data using advanced techniques, empowering you to extract valuable insights and make informed decisions.

Key Insights:

Data Ingestion:
- Witness Qubole’s seamless ingestion of raw Amazon review data from AWS using PI Spark, People, and H2O, laying the foundation for comprehensive analysis.
Initial Analysis:
- Explore the initial steps of data analysis, including a simple count of reviews and examination of available attributes, setting the stage for deeper insights.
Attribute Exploration:
- Dive into the exploration of various attributes within the dataset, such as marketplace, customer ID, product title, and star rating, uncovering valuable information for analysis.
Temporal Analysis:
- Gain insights into temporal trends by analyzing the distribution of reviews over time, enabling you to identify patterns and make strategic decisions.
Category Analysis:
- Discover the distribution of reviews across different product categories, providing valuable context for understanding consumer preferences and trends.
Sentiment Prediction:
- Explore Qubole’s sentiment prediction model, leveraging deep learning techniques to analyze text data and predict sentiment accurately.
Model Training and Evaluation:
- Witness the process of model training and evaluation, including grid search across various parameters, ensuring optimal performance.
Sentiment Projection:
- Experience the power of Qubole’s sentiment prediction model in action, enabling you to project sentiment for any text data and make data-driven decisions.

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